APLIKASI JARINGAN SYARAF TIRUAN UNTUK PERAMALAN HARGA SAHAM

Abstract

The capital market is an activity that is related to the public offering and trading of securities, public companies relating to issuance of securities, as well as institutions and professions related to the effect. Capital Markets acted as a liaison between investors, traders and the financial experts with companies or government institutions through trade instruments such as bonds over the long term, stocks, and other. In the course of an investor in the stock market, traders and the financial experts would certainly predict stock prices, it is very beneficial for investors, traders and the financial expert to be able to see how a company's stock investment in the future. Prediction of stock prices to anticipate the rise and fall of stock prices. With the prediction, is very helpful for investors, traders and the financial experts in decision making. Therefore required an application that can predict future stock prices based sequence (time series). With this application it is hoped will be able to shorten the time in predicting stock prices and help investors, traders and the financial expert in making a decision that will come. Systems development method using GRAPPLE (Guidelines for Rapid Application Engineering). This application uses 4 inputs, ie the highest price, lowest price, closing price and volume. This application provides the facility for 2 users are admin and user. This application can predict the closing stock price for the day. This application was built with back propagation neural network method using Java programming language, MySQL database management and NetBeans editor . Application of artificial neural networks for forecasting stock price to be built by utilizing artificial intelligence that serves to train the stock price data . Data shares in the past trained to obtain the weights and biases are used for forecasting future stock price. In forecasting the data used is the data prior to the date to do forecasting stock prices. The selection of weights and biases seen from the lowest MSE (Mean Square Error). Keywords : Stock Market , Stocks , Forecasting , Backpropagation